Abstract Metaheuristic algorithms are an upper level type of heuristic algorithms.They are known for their efficiency in solving many complex NP problems suchas time table scheduling, travelling salesman, telecommunication, geoscience andmany other scientific, economic and social problems. There are many metaheuristicalgorithms, but the most important one is the Genetic Algorithm (GA). What makesGA an exceptional metaheuristic algorithm is the ability to adapt to the problem tofind the most suitable solution that is the global optimal solution. Adaptability of GAis the result of the population consists of “chromosomes ” which are replaced witha new population by using genetics inspired operators of crossover (reproduction), and mutation .
The performance of the algorithm can be enhanced if hybridizedwith other heuristic algorithms which are sometime needed to slow the convergenceof GA toward the local optimal solution that can occur with some problems, and tohelp in obtaining the global optimal solution. GA is known to be very slow comparedto other known metaheuristic algorithms such as Simulated Annealing (SA)and this speed will further decrease when GA is hybridized (HyGA). To overcomethis issue it is important to change the structure of the chromosomes and the populationin general in order to create variable width chromosomes . This type of structureis called Hybrid Dynamic Genetic Algorithm (HyDyGA).
In this Chapter, GAis thoroughly covered including all improvements and changes which include hybridizationusing Hill-Climbing a heuristic algorithm. The new algorithms are calledHybrid GA (HyGA) and Hybrid Dynamic GA (HyDyGA). These improvements areused to solve a very complex NP problem the image segmentation process.
Usingmulticomponent images increases in the complexity of the problem of segmentationand puts more burden on the metaheuristic Genetic Algorithm. The efficiencyof HyGA and HyDyGA in the segmentation process of multicomponent images isproved using collected field samples and it can reach more than 97%.